37 Clinical utility of the BEARS as a sensitive screener for sleep problems in ADHD.
Notice bibliographique
Résumé
Objective: Many children and adolescents do not achieve adequate sleep durations. The prevalence of sleep problems has been estimated at 7% for typically developing children (Corkum, Tannock, & Moldofsky, 1998) and as high as 45% for representative samples of children, including participants with various diagnoses in proportion to what would be expected in the population (Sher-Fen Gau, 2006). For children with ADHD, the prevalence of sleep problems has been estimated at between 25-50% (Corkum, Tannock, & Moldofsky, 1998). Given the important role that sleep plays in children with ADHD, a brief and effective screener is needed to aid clinicians in assessing for sleep problems, especially when the referral for a neuropsychological evaluation concerns ADHD or any other neurodevelopmental disorder for which presenting concerns involve symptoms that overlap with ADHD. While the developers of the BEARS have demonstrated its utility as a screening tool, there is currently no independent published research replicating this finding. The current study aimed to replicate the findings of the BEARS developers by demonstrating its utility as a sensitive screening tool for sleep problems. It was predicted that the BEARS would demonstrate high sensitivity in identifying children with sleep problems. Participants and Methods: Data from 54 school aged children (aged 6-147-13, Mage = 9.83) was analysed. Children were administered the BEARS, and caregivers completed the BEARS and Children's Sleep Habits Questionnaire (CSHQ), as part of a larger study. Results: Binomial logistic regression model was statistically significant, x2(2) = 20.508, p < .0005. The model explained 46.8% (Nagelkerke R2) of the variance and correctly classified 70.8% of cases. Sensitivity was 78.6%, specificity was 60.0%, positive predictive value was 73.3%, and negative predictive value was 66.7%. Both predictor variables, parent reported BEARS (p = .001) and child-reported BEARS (p = .049), were significant. Children with higher BEARS parent report scores had 3.27 times higher odds, and those with higher self-report scores had 2.88 times higher odds, of exceeding the CSHQ cut-off than those with lower scores. ROC curve analysis revealed that the BEARS parent and self-report scores had excellent diagnostic utility (Hosmer et al., 2013) for accurately classifying children who exceeded the cut-off on the CSHQ from those who did not (area under the curve [AUC] = 0.849, SE = 0.054, 95% CI = .742 to .956, p < .001). Conclusions: The results of the current study indicate that the BEARS has excellent diagnostic utility for accurately classifying sleep problems. Additionally, it is quick to administer making it a practical screening tool for clinicians to include as part of a comprehensive neuropsychological assessment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».